Field
This disclosure is generally related to a recommendation system. More specifically, this disclosure is related to a general recommendation system capable of providing recommendations over multiple domains.
Related Art
As mobile devices equipped with technology to detect user context, such as physical surroundings, become more pervasive in our everyday lives, context-aware recommendation applications that detect and make use of physical surroundings can increasingly contribute to improving the lifestyle of mobile device users. Such software includes context-aware systems that may adapt to the computing environment, including physical surroundings, and make recommendations based on the physical surroundings. A context-aware system on a mobile device detects the computing environment and adapts to changing conditions detected from the environment, such as location and movement of the mobile device, nearby devices, and other surrounding conditions. Additional user context can also be extracted from various user events, such as web pages or documents viewed by the user, the user's past activities, interest expressed by the user, etc.
Such context-aware systems may establish various user models based on different user contexts. A user model can be used to describe user behaviors and interests. A context-based recommendation system may then recommend activities, such as leisure activities, based on a user model. However, providing a context-based recommendation system that can include a large number of potentially recommendable items and support a large number of concurrent users can be computationally challenging. Moreover, conventional recommendation systems often are not able to incorporate different user models.